Elevated fluoride (F⁻) levels in groundwater, primarily due to geogenic processes, pose significant health risks, including dental and skeletal fluorosis and neurological disorders. This study aimed to quantify source-dependent F⁻ exposure at the community level in selected tropical dry regions of Andhra Pradesh, India. These locations include Chintal Cheruvu, Rompicharala, Shantamangalur, Thimmapur, and Nadendla. Community surveys and drinking water sample analyses were conducted in these regions. Dental Fluorosis Index (DFI) was used to estimate exposure levels across age and sex groups. Findings of surveys indicate that groundwater consumption with high F⁻ (4.3 mg/L) results in the highest exposure dose (0.62 mg/kg/day), with Chintal Cheruvu identified as the most affected. A strong positive correlation was observed between exposure dose, water F⁻ content, and the Community Fluorosis Index (CFI), with R² values of 0.98 and 0.97, respectively. Dental fluorosis prevalence exceeded 80% across all age groups, and household surveys revealed 100% unawareness of F⁻ exposure risks. Though there exist many ways to determine the impact of fluoride, the hierarchy of regions may change with the type of parameter chosen. To address this, we developed the Fluoride Impact Index (FII), a multi-criteria index computed considering various parameters indicating the impact of fluoride in a region. The magnitude of FII for Chintal Cheruvu is 0.563 which is highest among the considered regions indicating that it is most impacted region that needs remedial measures first in the hierarchy. Rompicharala with FII as 0.252, Nadendla (0.223), Shantamangalur (0.214), and Thimmapur (0.188) follows the hierarchy. These findings highlight the urgent need to raise awareness about F⁻ exposure risks and to identify sustainable alternative water sources. Immediate interventions, including human health risk assessments using the USEPA approach and the provision of safe drinking water, are critical to achieving SDG-6 of safe drinking water for all by 2030. © 2024

Climate change poses a risk to the human societies and environment, encouraging a shift towards clean energy sources. Among these sources, offshore wind energy emerges as a favorable solution, due to its steady and strong wind resources, coupled with mature technology. Establishing offshore wind farms requires substantial financial investment. However, uncertainties induced by climate change may not only impact the cost-effectiveness of offshore wind farms but also influence the suitability of regions for their development. Therefore, the present study presents a novel framework for identifying optimal regions for off-shore wind farms by considering future projections under the various Shared Socioeconomic Pathway (SSP) scenarios. A weighted multi-model ensemble (MME) of ten CMIP6 climate models was considered. Offshore wind energy resource are classified based on resource richness, stability, risk, and economic viability. Criteria Importance Through Intercriteria Correlation (CRITIC) method is used to assign weights to each factor, offering insights into their influence on wind resources. The findings reveal that projections for the SSP2-4.5 and SSP5-8.5 scenarios show that the western and northeastern offshore regions within the study areas have emerged as the top-ranking regions due to their abundant wind energy resources and favorable stability, risk and economic factors. By employing a novel methodology, this study produces suitability maps that identify promising wind regions for future development, providing important information for long-term planning in India’s offshore wind sector

Renewable energy sources, free of environmental risks, are vital for achieving net-zero CO2 emissions and addressing climate change to meet Sustainable Development Goals. This study explores the evolution of solar energy research using bibliographic coupling and keyword co-occurrence analysis of 6,460 articles from 1988 to 2024. The findings reveal a significant increase in solar power-related publications, with China leading in research output, followed by the United States and India. Top journals include Renewable Energy and Energies, with a growing focus on Energy and Engineering. This analysis serves as a vital reference for solar energy researchers and professionals

Plastic-aggregates are made up from unused or waste plastic and natural aggregates which have recently been emerged as a significant addition to the existing emerging contaminants list mainly in the coastal environment. The transformation from plastics/microplastics to Plastic-aggregates signifies a crucial shift in our understanding and use of plastics and prompting us to reconsider their fundamental characteristics along with possible environmental threats. When plastic waste is incinerated for the purpose of disposal, it combines with organic and inorganic substances present in the surrounding environment, leading to a new type of material. Besides, some natural factors (physical, chemical, biological or in combination) also act upon discarded plastics to combine with rocks and other earthen materials to form plastic-aggregates. Our research aims to build fundamental knowledge and critically review the possible formation process, classification, and possible degradation of all such polymer-rock compounds along with their impact on the ecosystem. The knowledge gap related to the degradation and release of secondary pollutants from these agglomerates is to be addressed urgently in future research. Development and standardization of proper sampling and reporting procedures for plastic-aggregates can enhance our understanding related to their impacts on human health as well as to the entire environment as these aggregates contain different toxic chemicals

Artificial intelligence (AI) has emerged as a prominent tool in the modern day. The utilization of AI and advanced language models such as chat generative pre-trained transformer (ChatGPT) and Bard is not only innovative but also crucial for handling challenges related to water research. ChatGPT is an AI chatbot that uses natural language processing to create humanlike conversations. ChatGPT has recently gained considerable public interest, owing to its unique ability to simplify tasks from various backgrounds. Similarly, Google introduced Bard, an AI-powered chatbot to simulate human conversations. Herein, we investigated how ChatGPT and Bard (AI powdered chatbots) tools can impact water research through interactive sessions. Typically, ChatGPT and Bard offer significant benefits to various fields, including research, education, scientific publications, and outreach. ChatGPT and Bard simplify complex and challenging tasks. For instance, 50 important questions about water treatment/desalination techniques and 50 questions about water harvesting techniques were provided to both chatbots. Time analytics was performed by ChatGPT 3.5, and Bard was used to generate full responses. In particular, the effectiveness of this emerging tool for research purposes in the field of conventional water treatment techniques, advanced water treatment techniques, membrane technology and seawater desalination has been thoroughly demonstrated. Moreover, potential pitfalls and challenges were also highlighted. Thus, sharing these experiences may encourage the effective and responsible use of Bard and ChatGPT in research purposes. Finally, the responses were compared from the perspective of an expert. Although ChatGPT and Bard possess huge benefits, there are several issues, which are discussed in this study. Based on this study, we can compare the abilities of artificial intelligence and human intelligence in water sector research. © 2023

The equitable deployment of Electric Vehicle Charging Infrastructure (EVCI) is essential to address range anxiety and ensure widespread adoption of electric vehicles. This paper aims to identify the unserved areas of Delhi in terms of public Electric Vehicle Charging Infrastructure (EVCI) using a novel accessibility analysis approach. This study addresses accessibility gaps to address the Delhi EV policy's ambitious target of providing 3000-m access to public EV charging stations. Enhanced Two-Step Floating Catchment Area (E2SFCA) method is employed to quantify the accessibility levels to EVCI's at 100 m grid level. Global Moran I and Local Moran I analysis is conducted to identify areas where intervention is required. The location-allocation models indicate that installing at least 105 additional EV charging stations in the urban core and 150 in the peri-urban fringes would allow 93 % of the population to achieve the accessibility targets and an additional service coverage of 176.6 km2. The proposed methodology aims to achieve equitable accessibility to ECVIs which would lead to a better match of the supply-demand gap hence leading to the successful implementation of these infrastructures. The optimized yet balanced growth methodology and case-study for EV charging network expansion presented in this study is expected to aid policymakers in ensuring equity and spatial distributive justice in transportation electrification efforts

Aerial inspection of solar PV plants using Unmanned Aerial Vehicles (UAVs) is gaining traction due to benefits such as no downtime and cost-effectiveness. This technology is proven to be the low-cost alternative to conventional approaches involving visual inspection and I-V curve tracing to identify physical damages and underperforming strings, respectively. Though the use of UAVs for thermographic solar PV inspection is a popular alternative in developed countries, its use in developing economies experience various challenges. Studies emphasizing these challenges especially in the context of rapid evolution of drones are limited. To overcome this limitation, literature scoping, a one-on-one survey, focus group discussion, and a flight campaign using a UAV with a thermal payload is conducted in India to identify the limitations. These are further categorized into Technical, Behavioural, Implementation, Pre-deployment, Deployment, and Post-deployment categories. The relevance and significance of each challenge are analysed using a hybrid multi-criteria framework developed in this study. Findings of this study highlight the importance of drone regulations, technology readiness, and workshops for drone pilots, industry professionals, and solar developers in India. This study aid developing economies in devising strategies that can promote the use of UAVs for solar PV plant commissioning activities.

Aerial ropeway transit (ART) systems are emerging alternatives to augment existing transit systems in congested cities in the Global South, especially in urban areas with limited transit coverage because of road width constraints or topography. Integration of aerial cable car stations to an existing transit network can improve the overall accessibility of various population segments with significant positive benefits in relation to reducing transport-related social exclusion. This study evaluated the impact of introducing ART in the city of Varanasi (India) and assessed the spatial accessibility improvements to critical facility locations such as heritage sites, educational institutions, hospitals, and employment centers. Several multimodal transit expansion scenarios were considered in this study and the potential benefits of each case were quantified using the two-step floating catchment area (2SFCA) method. A multi-criteria decision-making (MCDM) approach was subsequently employed for identifying the optimal locations of ART stops. Microlevel analysis findings suggest that the mean accessibility values could increase up to 10.92% in the first phase of the ART implementation, which could subsequently increase to 24.7% and 49.8% for the subsequent transit expansion scenarios. The study also investigated the Varanasi ART DPR prepared by Varanasi Development Authority (VDA) and showed that a significant increase of 16% in accessibility levels could be achieved if optimal stop locations identified in this study were implemented. The proposed two-step (2SFCA+MCDM) method for identifying the optimal locations of ART stations in a multimodal transit network is expected to be an effective tool for transit system redesign using place-based accessibility measures.

Renewable energy sources are gaining prominence as eco-friendly and sustainable alternatives to fossil fuels due to their availability and minimal greenhouse gas emissions. Nonetheless, the critical challenge is the availability of renewable resources, which fluctuates with changes in climatic conditions. This limitation poses a consistent challenge to generating base load power if it relies solely on a single type of renewable resource. Addressing this, integrating multiple renewable sources into hybrid systems has emerged as a viable solution. This study presents a framework, integrating Geographic Information Systems (GIS) and Hybrid Multi-Criteria Decision Making (MCDM) techniques to identify plausible locations for the deployment of Hybrid Offshore Solar and Wind Power Plants (HOSWPP) and the developed framework is demonstrated considering Indian Exclusive Economic Zone (EEZ) as a study area. Using the proposed approach, Indian EEZ region is classified into five suitability classes. The effectiveness of regions within each class is further assessed in terms of complementarity measured using Kendall's coefficient. Findings suggested that Kendall's coefficient for highly suitable class is ?0.41 indicating the regions identified in this study are the prime locations for installing HOSWPP. A total of twenty optimal sites for HOSWPP deployment, predominantly in the offshore regions of Tamil Nadu and Gujarat. Eighteen sites are located along Kanyakumari to Thisayanvilai in Tamil Nadu, including areas in the Gulf of Mannar and near Valinokkam are found plausible. The rest of the two sites are in the offshore regions of Gujarat. This study provides a strategic roadmap to increase the renewable footprint, contributing to the global transition towards cleaner energy sources

The escalation in energy demand due to the rising population highlights the need for the transition toward sustainable power generation alternatives. In this context, floating solar photovoltaic (FPV) systems emerge as an innovative and environmentally friendly alternative, offering the dual benefits of energy generation and conservation of terrestrial resources. Based on ERA5 datasets, an in-depth analysis of the potential and efficiency of FPV systems, specifically within the Indian Exclusive Economic Zone (EEZ), is conducted in this study. Findings of this study evidence the substantial capacity of the Indian EEZ that could yield energy that is equivalent to 43 times of annual consumption by utilizing 10% of the EEZ region. A full-scale utilization of the EEZ for FPV systems could revolutionize the energy landscape, potentially generating 433 times the country's present annual energy requirements. A complete transition to such renewable energy sources within the EEZ is projected to result in an annual reduction of 595 billion metric tons in carbon emissions.

Population of India is growing exponentially thereby the necessity to enhance the power generation capacity is increasing. Considering the detrimental impacts of conventional approaches to generate electricity on the environment, it is imperative to minimize the dependency on fossil fuels and make a transition towards the use of renewable sources. Harnessing energy using floating solar photovoltaic modules is one of the promising renewable alternatives that can curtail carbon-dioxide emissions while meeting the required energy demand. In this study, governing parameters obtained from ECMWF ERA5 datasets are used to evaluate techno-economic feasibility of the floatovoltaic solar system at selected locations in Gujarat and Tamil Nadu. The suitability of these regions for installing floatovoltaic systems is assessed by analyzing crucial parameters such as panel temperature, solar power output, Capacity Factor (CF) and Levelized Cost of Energy (LCOE). Findings depict that a total of 991 and 880 TWh of electricity can be generated with a capacity factor of 26.9% and 23.8% at Gujarat and Tamil Nadu locations, respectively, with an installed capacity of 420 MW floatovoltaic system. Implementation of this alternative renewable source can curtail carbon emissions by more than 700 billion metric tons at each location, minimizing the detrimental impact on the environment. Economic analysis reveals LCOE value at the Gujarat and Tamil Nadu locations is 0.072 and 0.08 USD/kWh, respectively. Promoting the adoption and installation of floatovoltaics can help India to meet its goal of net-zero emissions by 2050 and be self-sufficient in terms of energy.

Together with a human-centered approach to designing and operating production and logistics in an industrial context, digital technologies can lead to a sustainable, resilient, and human-centric Industry 5.0 (I5.0). This article is one of the first interdisciplinary studies integrating digital technologies and circular economy (CE) concepts in I5.0. Using expert-based surveys of industry leaders and analytical hierarchical process techniques, the article advances CE and technology management by empirically investigating the influence of I5.0 on CE aspects in manufacturing. The novel results presented here can enable policymakers and industry leaders to design effective CE strategies.

Increasing the footprint of installed renewable energy capacity helps to mitigate CO 2 emissions. Numerous countries have been devising strategies for harnessing various renewable sources to meet the rising demands. Solar, wind, hydro, geothermal, and biomass are a few of the renewable sources that are being used to generate electricity across the globe. Based on the nature of the source's availability, biomass is considered one of the plausible resources to generate electricity consistently. In light of this, extensive research works is being conducted to explore different approaches to convert biomass to energy. Microwave pyrolysis is one of the new approaches to convert biomass into energy. This study aims to understand the global trends in adopting micro-wave-assisted pyrolysis with the help of bibliometric analysis performed based on keyword occurrences. A total of 510 scientific contributions have been made between 1979 and 2023, addressing various aspects of microwave-assisted pyrolysis to convert biomass into energy. To gain insights into the adaptability of microwave-assisted pyrolysis, temporal growth in the total number of publications and citations has been studied. Prominent publications, top journal sources, highly contributing countries, and researchers are also identified to facilitate future research in this area. Findings suggest that attention to microwave pyrolysis is increasing by 7.59%, and China is designated as the top nation and the most frequent partner in microwave-assisted pyrolysis of biomass, followed by the United States and Malaysia. Bioresource Technology, Journal of Analytical and Applied Pyrolysis, Fuel, and Energy are popular journals focusing on microwave-assisted pyrolysis. Based on the bibliometric study of prior existing work, this study presents a road map for collaborations to conduct research on microwave-assisted pyrolysis of biomass to generate energy.

Workplace energy consumption exceeds household usage, due to which, even small changes in workplace energy behaviour can minimise emissions associated with energy consumption. Despite global workplace energy conservation efforts, measuring progress is impeded due to the involved complexity. Building on this gap, this study developed Workplace energy conservation index (WECI), that can assist a company in measuring the attainment of energy conservation with respective to the benchmarking company. The proposed index is built by considering individual and organizational enablers. A total of 20 enablers identified through extensive literature review complemented with the outcomes of focus group discussions are the components of developed index. For demonstration of the proposed WECI, a target company and a benchmarking company from the automobile sector have been selected and the involved computations are expounded. Findings suggest that the attainment of target company is 46 % indicating the scope of improvement. Detailed evaluation of WECI guides the stakeholders to identify the thrust area that can improve the attainment of energy conservation at the workplace. The proposed framework can be extended to companies in other sectors where the relevant enablers can be added in the phase of focus group discussions.

Hydrogen is the potential future resource to cater the energy and chemical requirements. Microwave-assisted pyrolysis (MAP) could be the potential technology to obtain green hydrogen from lignocellulosic biomass waste. The proximate and elemental composition varies with the type of lignocellulosic biomass, which influences the yield of hydrogen. In MAP, the operating parameters including microwave power, heating rate, temperature, and susceptor play an important role in hydrogen production. Cellulose, hemicellulose, and lignin present in the lignocellulosic biomass undergo decomposition when they are subjected to MAP. Most importantly, the susceptor material added to the feedstock induces the plasma, which would help the cleavage of the bonds to form hydrogen gas. When the microwave power intensity is high, then the generation of hydrogen would be high. During the MAP, the formed char from the biomass would act as susceptor cum catalyst, hence it further speeds up the hydrogen generation pathways. The energy and time required for the MAP are very less compared to conventional pyrolysis. The present review manuscript would help the research community to understand the possible applications of MAP for hydrogen production.

Biomass pyrolysis is the most effective process to convert abundant organic matter into value-added products that could be an alternative to depleting fossil fuels. A comprehensive understanding of the biomass pyrolysis is essential in designing the experiments. However, pyrolysis is a complex process dependent on multiple feedstock characteristics, such as biomass consisting of volatile matter, moisture content, fixed carbon, and ash content, all of which can influence yield formation. On top of that, product composition can also be affected by the particle size, shape, susceptors used, and pre-treatment conditions of the feedstock. Compared to conventional pyrolysis, microwave-assisted pyrolysis (MAP) is a novel thermochemical process that improves internal heat transfer. MAP experiments complicate the operation due to additional governing factors (i.e. operating parameters) such as heating rate, temperature, and microwave power. In most instances, a single parameter or the interaction of parameters, i.e. the influence of other parameter integration, plays a crucial role in pyrolysis. Although various studies on a few operating parameters or feedstock characteristics have been discussed in the literature, a comprehensive review still needs to be provided. Consequently, this review paper deconstructed biomass and its sources, including microwave-assisted pyrolysis, and discussed the impact of operating parameters and biomass properties on pyrolysis products. This paper addresses the challenge of handling multivariate problems in MAP and delivers solutions by application of the machine learning technique to minimise experimental effort. Techno-economic analysis of the biomass pyrolysis process and suggestions for future research are also discussed.

Groundwater salinization of coastal aquifers as a result of climate change and anthropogenic activities is a widely acknowledged phenomenon. Sundarbans, in India is one such area where this phenomenon is noticed at an unprecedented rate making drinking water unpotable for consumption. Studies identifying the prime drivers causing this detrimental phenomenon are limited as the existing studies explicitly lack analyzing the holistic view. Building on this gap, this study aims to conduct a systematic literature review and identify the list of drivers that are promoting groundwater salinization. The influence of wide range of parameters depicting the climate change i.e., varying rainfall pattern, sea level rise (SLR), El Nino-Southern Oscillation (ENSO) and tropical cyclones (TC) on qualitative and quantitative variations in the groundwater at various temporal scales is studied with the help attributes collected from literature. The study reveals a significant drop in groundwater levels (GWL) between 1996 and 2017. This depletion is noted to be primarily attributed to variations in the Indian Ocean Dipole (IOD) and El Niño Southern Oscillation (ENSO), affecting rainfall patterns and recharge rates. During tropical cyclones, GWL rapidly raised, while it is noted that the groundwater quality is sensitive to ENSO. Sea-level rise, changing rainfall patterns, and increasing population density exacerbate groundwater salinization. Existing sources of water, i.e., shallow aquifers exhibit high salinity, and deep aquifers exceed permissible limits. The study evidences the needs to address drinking water scarcity and potential migration resulting from these complex interactions between climate, population, and groundwater management.

Harnessing offshore wind energy helps to achieve carbon neutrality. However, the availability of wind resources is sensitive to climate change and also depends on the available foundation technologies of wind turbines. Investigating annual energy production (AEP) and CO 2 equivalent emission avoidance using offshore wind farms helps to make appropriate energy strategies. This study uses an ensemble developed using CORDEX-South Asia regional climate models by assigning weights derived from the CRITIC multi-criteria technique to estimate AEP under two representative concentration pathways (RCP), i.e., RCP4.5 and RCP8.5 scenarios in the North Indian Ocean. To account for the impact of climate change, inter and intra-annual variations in the wind power density (WPD), capacity factor (CF), and AEP are estimated. Estimates based on the feasibility of foundation technology show that the cumulative AEP obtained from the 240 MW wind farm in historic, near- and far-future scenarios are 357.91 TWh, 808.6 TWh, and 4888.78 TWh, respectively. In the near future, harnessing offshore wind energy can reduce CO 2 emissions by 4500 million tons annually. The findings of this study suggest that harnessing offshore wind energy by installing farms within the study area could help in the massive reduction of CO 2 emissions leading to carbon neutrality.

Integrating Unmanned Aerial Vehicle (UAV) technology with Artificial Intelligence AI and Computer Vision has revolutionized asset management, particularly pavement health monitoring. However, current AI-based methods often struggle in low-visibility scenarios, limiting their effectiveness. To address this, we present a novel end-to-end deep learning pipeline that detects image degradation using an efficient Attention mechanism and performs subsequent enhancement. This algorithm can be seamlessly integrated into drones or used for post-processing of pavement imagery. Its efficiency allows for scalability, making it a valuable tool for downstream road health monitoring tasks, such as cost estimation for road repairs. Our approach achieves mean accuracies of 93.34% with a mean inference time of 0.154 sec., demonstrating its efficacy.

Technological advancements can accelerate the attainment of Sustainable Development Goals (SDGs). However, technology adoption is associated with complex, interrelated factors, even more so in the context of rural areas in emerging economies. We examine the adoption of one technology that can be crucial for resolving water scarcity issues facing countries around the world–the Rainwater Harvesting (RWH) technology and the critical success factors (CSFs) that promote its adoption in rural India. Building on an extensive literature review, focus group discussions, and field visits, this paper identifies a list of factors that promote its adoption. To derive the CSFs, the relevance of each factor is analysed using Fuzzy-Delphi, and the significance is determined using D-DEMATEL technique. The novel results presented here suggest that awareness about RWH technologies, their perceived usefulness, ease of use, and tax incentives for companies are some crucial factors that can increase RWH technology adoption. Furthermore, community-based workshops explaining the architecture and operational aspects of the RWH System as well as simplifying the RWH system architecture can accelerate its usage in rural areas. Based on these results, the paper presents a new roadmap for leveraging technology to attain SDGs in rural areas of developing countries.

The Earth-Air-Heat-Exchanger (EAHE) is an effective solution for reducing energy demand. GIS based tools are commonly used to assess the suitability of EAHE sites, relying on geospatial data for geological and climatic parameters. However, lack of comparable data for different regions limits their applicability. In this regard, a framework that utilizes ERA5 reanalysis data to derive necessary geological and climatic parameters is proposed and demonstrated by considering India. Findings indicate that 25% of country's area falls under excellent category, benefiting 21% of the population. Additionally, 47% and 32% of the area are classified as moderate and good, respectively, providing thermal comfort to 51% and 28% of the population. Technical suitability of installing EAHE in an excellent category region is assessed through design and simulation study. Field studies are performed to collect climatic and geological parameters required for design. A computer model is developed using these design variables to determine the outlet temperature from EAHE. The simulation studies align with site suitability maps generated using GIS-MCDM framework, highlighting its reliability. Carbon footprint analysis reveals that EAHE adoption can reduce CO2 by 66.2% compared to conventional air conditioning units. The proposed GIS-MCDM framework can be extended to other regions lacking field data.

Technological advances can significantly transform agrarian rural areas by increasing productivity and efficiency while reducing labour intensive processes. For instance, the usage of Unmanned Aerial Vehicles (UAVs) can offer flexibility collecting real-time information of the crops enabling farmers to take timely decisions. However, little is known about the barriers to the adoption of such technologies by rural farmers in emerging economies like India. Building on an extensive literature review, focussed group discussions, and field visits, the barriers impacting the adoption are identified and classified into technical, social, behavioural, operational, economic, and implementation categories. The relevance of each barrier and its importance is evaluated using a hybrid multi-criteria framework built on the theory of Fuzzy Delphi and Fuzzy Analytical Hierarchy Process to identify the most crucial barriers to the adoption of UAVs to implement precision agriculture in rural India. The paper suggests new avenues for accelerating technology adoption in rural areas of emerging economies.

Offshore wind energy assessments help in identifying suitable locations for offshore wind farms. Its importance is further amplified in the context of climate change as wind power potential is susceptible to it. The present study aimed to assess the offshore wind potential of India and its sensitivity to climate change with the help of two different ensemble variants developed using nine CMIP6 Global Climate Models (GCMs). First ensemble is created with equal emphasis on all GCMs, while differential weights derived using Shannon entropy technique is used to develop the other ensemble. Created ensembles are further compared with ERA5 data. Comparative results suggest that differential weighted ensemble is superior to uniform weights in terms of bias. Owing to this, weighted ensemble is further used to study the impact of climate change on wind power density (WPD) for the near (2021–2045) and far-future (2075–2099) periods under two shared socioeconomic pathways (SSP) scenarios, i.e., SSP2-4.5 and SSP5-8.5, the most widely used and probable scenarios. Findings suggest that WPD variation in the study area ranges between ?10% and ?20%. These variations are examined to study further the impact of climate change on geographical variations of WPD distributions. With the regions in Arabian Sea as an exception, WPD appears to increase in future scenarios. WPD varies more in far-future scenarios compared to near-future scenarios. The future variations of the WPD across study areas are prominent in the case of SSP5 - 8.5 compared to the variations noted in the case of SSP2-4.5. Findings of this study help stakeholders to understand the impact of climate change on offshore wind potential. Moreover, plots showing the variation of WPD for near and far-future scenarios complemented with additional studies help in choosing an appropriate location to tap the offshore wind potential in India.

Pavement monitoring involves periodic damage detection and condition assessment of pavements for efficient pavement management. Unmanned aerial vehicle (UAV)-based pavement monitoring requires multidisciplinary knowledge of pavement distress, drone type, payload, flight parameters, drone deployment, and image processing. Owing to the availability of various UAVs, data sensing devices, operating ecosystems, and post-processing tools, selecting an appropriate combination of these systems is crucial. Therefore, the primary objective of this study is to provide essential knowledge on the prevalent challenges of existing monitoring techniques and discuss the potential advantages of UAVs over conventional pavement monitoring practice. A state-of-the-art review emphasizing UAV technicalities in the context of image-based pavement monitoring is presented. A detailed workflow and checklist for drone deployment is drafted for novice users to ensure safe and high-quality data acquisition. Finally, the present challenges and future scope of UAV-based pavement monitoring is discussed. Overall, this study aims to provide inclusive and comprehensive information on UAV-based pavement monitoring to beginner researchers.

The amalgamation of Unmanned Aerial Vehicle (UAV) based systems with models built on Artificial Intelligence (AI) and Computer Vision approaches have enabled several applications in urban planning and smart cities, such as remote health monitoring of roads and infrastructure. However, most of such existing models are trained and evaluated for clear lighting conditions, and they do not perform well under low visibility. This work proposes a fast and lightweight approach for deployment on UAV-based systems that can (i) detect the low-visibility condition in a road image captured by a UAV, and (ii) alleviate it and enhance the quality of the road image. The proposed approach achieves state-of-the-art results and thus establishes itself as an essential precursor to downstream Computer Vision tasks related to remote monitoring of roads, such as identification of different distress conditions.

Rooftop rainwater harvesting systems (RRWHS) effectively provide water access by storing precipitated water. The amount of water harvestable using these systems is proportional to the availability of rooftop areas in the region. The use of satellite imagery has gained traction in recent times considering the challenges in conducting a manual survey to determine the rooftop area. However, the limitations on spatial resolution impaired stakeholders from conducting similar assessments in areas with small residential units. In this regard, the use of unmanned aerial vehicles (UAVs) providing high-resolution spatial imagery for the delineation of rooftops of all scales has become popular. The present study is an attempt to utilize UAV-generated orthomosaics to estimate the harvestable quantity of rainwater for setting up an RRWHS. A study area in the Gurgaon district, India, is selected, and the steps involved in estimating the quantity of water harvestable using UAVs are demonstrated. In addition to these computations, a suitable site for constructing the storage unit is identified with the aid of a weighted overlay technique implemented using a Geographic Information System. The results from the study show that nearly 11,229 m of water can be harvested per year in the study site using the RRWHS.

Leadership helps to build strong organizations with resilient cultures. It is established that leadership needs a transition powered by digital technologies to tackle the shift from workplace culture to remote work, which is being practiced even after the pandemic to reduce operational costs and improve flexibility. The transition from leadership to e-leadership requires a profound understanding of the critical success factors (CSFs). The primary objective of this study is to identify the critical success factors of e-leadership using a systematic literature review and questionnaire survey technique. The identified CSFs are grouped under (i) Technology Management, (ii) E-Motivation and well-being, and (iii) E-change management categories. The Fuzzy Delphi technique is used to find the relevant CSFs and the relative dominance of each CSF category; the CSFs are then analyzed using the fuzzy analytical hierarchy process. The results suggest that employee engagement using digital technologies is the most critical success factor, while role clarity has relatively the least significance for the transition to take place. The findings of this study facilitate the smooth transition from leadership to e-leadership.

Streetscape design can encourage social interaction and community building, creating a sense of place and improving the overall well-being of the resident community. Detailed investigation of streetscape quantitatively can identify the opportunities to reduce energy use, improve air quality, and enhance the natural environment. Data derived from street view services are typically used to analyze the streetscape. However, the availability of street view services is limited to selected regions, because of which conducting a study for an area deprived of street view services is a challenge. Building on this gap, this study proposes a new system introduced as Urban scan to overcome the limitation. •The proposed system can capture the streetscape in 360°.•Helps to analyze the streetscape composition with the least computational effort.•The accuracy of the classification is tested with different datasets and is noted to be above 96.02%.

Biomass is a viable alternative to fossil fuels due to the abundant availability of solid waste and the associated greenhouse gas emissions. Various conversion methods, including physical, thermal, biochemical-microbial, and chemical processes, have been utilized to convert biomass to energy. Microwave-assisted pyrolysis (MAP) is one of the prominent techniques to convert biomass into energy. Various parameters affect the yield and quality of the product in MAP. Studies addressing comprehensive insight into all influencing parameters are limited. Moreover, the relative hierarchy of the parameters is not evaluated in any of the past research works. Considering this limitation, this study proposed a two-step approach based on a multi-criteria technique that aid stakeholders to analyze the significance of each parameter. The proposed approach is built on the theory of Fuzzy Delphi and the Analytical Hierarchy Process. A total of 27 different parameters affecting MAP are identified through extant literature. Analysis based on the proposed approach suggests that microwave power is the most significant parameter influencing MAP. The impact of co-processing feedstock is very minimal among all the identified parameters. The relative hierarchy of all the parameters drawn in this study help stakeholders performs MAP with the least resources.